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Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema. [ABSTRACT FROM AUTHOR]

The teaching-learning-based optimization (TLBO) algorithm is a population-based optimization algorithm which is based on the effect of the influence of a teacher on the output of learners in a class. A variant of teaching-learning-based optimization (TLBO) algorithm with differential learning (DLTLBO) is proposed in the paper. In this method, DLTLBO utilizes a learning strategy based on neighborhood search of teacher phase in the standard TLBO to generate a new mutation vector, while utilizing a differential learning to generate another new mutation vector. Then DLTLBO employs the crossover operation to generate new solutions so as to increase the diversity of the population. By the integration of the local search and the global search, DLTLBO achieves a tradeoff between exploration and exploitation. To demonstrate the effectiveness of our approaches, 24 benchmark functions are used for simulating and testing. Moreover, DLTLBO is used for parameter estimation of digital IIR filter and experimental results show that DLTLBO is superior or comparable to other given algorithms for the employed examples. [ABSTRACT FROM AUTHOR]